import numpy as np 
import pandas as pd 
import bokeh
from autoviz.AutoViz_Class import AutoViz_Class
import matplotlib.pyplot as plt
%matplotlib inline
import klib
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter(action='ignore', category=FutureWarning)

music = pd.read_csv("features_30_sec.csv")
music.head()
filename length chroma_stft_mean chroma_stft_var rms_mean rms_var spectral_centroid_mean spectral_centroid_var spectral_bandwidth_mean spectral_bandwidth_var ... mfcc16_var mfcc17_mean mfcc17_var mfcc18_mean mfcc18_var mfcc19_mean mfcc19_var mfcc20_mean mfcc20_var label
0 blues.00000.wav 661794 0.350088 0.088757 0.130228 0.002827 1784.165850 129774.064525 2002.449060 85882.761315 ... 52.420910 -1.690215 36.524071 -0.408979 41.597103 -2.303523 55.062923 1.221291 46.936035 blues
1 blues.00001.wav 661794 0.340914 0.094980 0.095948 0.002373 1530.176679 375850.073649 2039.036516 213843.755497 ... 55.356403 -0.731125 60.314529 0.295073 48.120598 -0.283518 51.106190 0.531217 45.786282 blues
2 blues.00002.wav 661794 0.363637 0.085275 0.175570 0.002746 1552.811865 156467.643368 1747.702312 76254.192257 ... 40.598766 -7.729093 47.639427 -1.816407 52.382141 -3.439720 46.639660 -2.231258 30.573025 blues
3 blues.00003.wav 661794 0.404785 0.093999 0.141093 0.006346 1070.106615 184355.942417 1596.412872 166441.494769 ... 44.427753 -3.319597 50.206673 0.636965 37.319130 -0.619121 37.259739 -3.407448 31.949339 blues
4 blues.00004.wav 661794 0.308526 0.087841 0.091529 0.002303 1835.004266 343399.939274 1748.172116 88445.209036 ... 86.099236 -5.454034 75.269707 -0.916874 53.613918 -4.404827 62.910812 -11.703234 55.195160 blues

5 rows × 60 columns

music.drop(columns=["filename"], inplace=True)
music.shape

(1000, 59)
plt.figure(figsize=(16,4))
sns.heatmap(music.isnull(), yticklabels=False, cbar=False, cmap="viridis")
plt.title("Music DataFrame Null Values")

Text(0.5, 1.0, 'Music DataFrame Null Values')
music["label"].value_counts()

blues        100
classical    100
country      100
disco        100
hiphop       100
jazz         100
metal        100
pop          100
reggae       100
rock         100
Name: label, dtype: int64
AV = AutoViz_Class()
target = "label"
sep=","
filename = "features_30_sec.csv"
dft = AV.AutoViz(filename, sep, target, "",
           header=0, verbose=0,
            lowess=False,chart_format='svg',max_rows_analyzed=150000,max_cols_analyzed=100)


Shape of your Data Set loaded: (1000, 60)
############## C L A S S I F Y I N G  V A R I A B L E S  ####################
Classifying variables in data set...
    59 Predictors classified...
        1 variables removed since they were ID or low-information variables

################ Multi_Classification VISUALIZATION Started #####################

Total Number of Scatter Plots = 1653

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/formatters.py:339, in BaseFormatter.__call__(self, obj)
    337     pass
    338 else:
--> 339     return printer(obj)
    340 # Finally look for special method names
    341 method = get_real_method(obj, self.print_method)

File ~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/pylabtools.py:151, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    148     from matplotlib.backend_bases import FigureCanvasBase
    149     FigureCanvasBase(fig)
--> 151 fig.canvas.print_figure(bytes_io, **kw)
    152 data = bytes_io.getvalue()
    153 if fmt == 'svg':

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py:2289, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2282     bbox_inches = rcParams['savefig.bbox']
   2284 if (self.figure.get_constrained_layout() or
   2285         bbox_inches == "tight"):
   2286     # we need to trigger a draw before printing to make sure
   2287     # CL works.  "tight" also needs a draw to get the right
   2288     # locations:
-> 2289     renderer = _get_renderer(
   2290         self.figure,
   2291         functools.partial(
   2292             print_method, orientation=orientation)
   2293     )
   2294     with getattr(renderer, "_draw_disabled", nullcontext)():
   2295         self.figure.draw(renderer)

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py:1544, in _get_renderer(figure, print_method)
   1541     print_method = getattr(
   1542         figure.canvas._get_output_canvas(None, fmt), f"print_{fmt}")
   1543 try:
-> 1544     print_method(io.BytesIO())
   1545 except Done as exc:
   1546     renderer, = figure._cachedRenderer, = exc.args

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py:1648, in _check_savefig_extra_args.<locals>.wrapper(*args, **kwargs)
   1640     _api.warn_deprecated(
   1641         '3.3', name=name, removal='3.6',
   1642         message='%(name)s() got unexpected keyword argument "'
   1643                 + arg + '" which is no longer supported as of '
   1644                 '%(since)s and will become an error '
   1645                 '%(removal)s')
   1646     kwargs.pop(arg)
-> 1648 return func(*args, **kwargs)

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/_api/deprecation.py:412, in delete_parameter.<locals>.wrapper(*inner_args, **inner_kwargs)
    402     deprecation_addendum = (
    403         f"If any parameter follows {name!r}, they should be passed as "
    404         f"keyword, not positionally.")
    405     warn_deprecated(
    406         since,
    407         name=repr(name),
   (...)
    410                  else deprecation_addendum,
    411         **kwargs)
--> 412 return func(*inner_args, **inner_kwargs)

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:540, in FigureCanvasAgg.print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    490 @_check_savefig_extra_args
    491 @_api.delete_parameter("3.5", "args")
    492 def print_png(self, filename_or_obj, *args,
    493               metadata=None, pil_kwargs=None):
    494     """
    495     Write the figure to a PNG file.
    496 
   (...)
    538         *metadata*, including the default 'Software' key.
    539     """
--> 540     FigureCanvasAgg.draw(self)
    541     mpl.image.imsave(
    542         filename_or_obj, self.buffer_rgba(), format="png", origin="upper",
    543         dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:431, in FigureCanvasAgg.draw(self)
    429 def draw(self):
    430     # docstring inherited
--> 431     self.renderer = self.get_renderer(cleared=True)
    432     # Acquire a lock on the shared font cache.
    433     with RendererAgg.lock, \
    434          (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
    435           else nullcontext()):

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:447, in FigureCanvasAgg.get_renderer(self, cleared)
    444 reuse_renderer = (hasattr(self, "renderer")
    445                   and getattr(self, "_lastKey", None) == key)
    446 if not reuse_renderer:
--> 447     self.renderer = RendererAgg(w, h, self.figure.dpi)
    448     self._lastKey = key
    449 elif cleared:

File ~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:93, in RendererAgg.__init__(self, width, height, dpi)
     91 self.width = width
     92 self.height = height
---> 93 self._renderer = _RendererAgg(int(width), int(height), dpi)
     94 self._filter_renderers = []
     96 self._update_methods()

ValueError: Image size of 1080x229824 pixels is too large. It must be less than 2^16 in each direction.
<Figure size 1080x229824 with 1596 Axes>
Time to run AutoViz = 813 seconds 

 ###################### AUTO VISUALIZATION Completed ########################


from pycaret.classification import *
clf = setup(music, target = 'label')

  Description Value
0 session_id 8693
1 Target label
2 Target Type Multiclass
3 Label Encoded blues: 0, classical: 1, country: 2, disco: 3, hiphop: 4, jazz: 5, metal: 6, pop: 7, reggae: 8, rock: 9
4 Original Data (1000, 59)
5 Missing Values 0
6 Numeric Features 58
7 Categorical Features 0
8 Ordinal Features 0
9 High Cardinality Features 0
10 High Cardinality Method None
11 Transformed Train Set (699, 58)
12 Transformed Test Set (301, 58)
13 Shuffle Train-Test True
14 Stratify Train-Test False
15 Fold Generator StratifiedKFold
16 Fold Number 10
17 CPU Jobs -1
18 Use GPU 0
19 Log Experiment 0
20 Experiment Name clf-default-name
21 USI e8bc
22 Imputation Type simple
23 Iterative Imputation Iteration None
24 Numeric Imputer mean
25 Iterative Imputation Numeric Model None
26 Categorical Imputer constant
27 Iterative Imputation Categorical Model None
28 Unknown Categoricals Handling least_frequent
29 Normalize 0
30 Normalize Method None
31 Transformation 0
32 Transformation Method None
33 PCA 0
34 PCA Method None
35 PCA Components None
36 Ignore Low Variance 0
37 Combine Rare Levels 0
38 Rare Level Threshold None
39 Numeric Binning 0
40 Remove Outliers 0
41 Outliers Threshold None
42 Remove Perfect Collinearity 1
43 Remove Multicollinearity 0
44 Multicollinearity Threshold None
45 Remove Perfect Collinearity 1
46 Columns Removed Due to Multicollinearity []
47 Clustering 0
48 Clustering Iteration None
49 Polynomial Features 0
50 Polynomial Degree None
51 Trignometry Features 0
52 Polynomial Threshold None
53 Group Features 0
54 Feature Selection 0
55 Feature Selection Method classic
56 Features Selection Threshold None
57 Feature Interaction 0
58 Feature Ratio 0
59 Interaction Threshold None
60 Fix Imbalance 0
61 Fix Imbalance Method SMOTE 

compare_models()
  Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
catboost CatBoost Classifier 0.8241 0.9811 0.8239 0.8444 0.8240 0.8045 0.8070 27.2530
rf Random Forest Classifier 0.7841 0.9701 0.7837 0.8034 0.7810 0.7600 0.7626 0.1220
lightgbm Light Gradient Boosting Machine 0.7841 0.9697 0.7839 0.7972 0.7814 0.7599 0.7620 2.9260
gbc Gradient Boosting Classifier 0.7683 0.9674 0.7676 0.7908 0.7659 0.7423 0.7451 2.3450
xgboost Extreme Gradient Boosting 0.7641 0.9650 0.7631 0.7760 0.7593 0.7376 0.7398 0.9830
et Extra Trees Classifier 0.7568 0.9631 0.7540 0.7733 0.7512 0.7296 0.7325 0.0900
lda Linear Discriminant Analysis 0.6997 0.9465 0.7005 0.7205 0.6961 0.6661 0.6687 0.0140
ridge Ridge Classifier 0.6238 0.0000 0.6232 0.6403 0.6105 0.5816 0.5863 0.0060
dt Decision Tree Classifier 0.5765 0.7644 0.5788 0.5868 0.5661 0.5291 0.5326 0.0130
nb Naive Bayes 0.4105 0.8445 0.4082 0.4103 0.3696 0.3439 0.3560 0.0080
lr Logistic Regression 0.3849 0.7952 0.3839 0.3580 0.3506 0.3156 0.3204 0.6730
qda Quadratic Discriminant Analysis 0.3363 0.6964 0.3148 0.3896 0.2679 0.2541 0.3092 0.0100
ada Ada Boost Classifier 0.3276 0.7332 0.3179 0.2431 0.2571 0.2495 0.2711 0.0830
knn K Neighbors Classifier 0.2674 0.6757 0.2651 0.2838 0.2615 0.1854 0.1873 0.0110
svm SVM - Linear Kernel 0.1789 0.0000 0.1829 0.1006 0.0979 0.0917 0.1199 0.0140
dummy Dummy Classifier 0.1130 0.5000 0.1000 0.0128 0.0230 0.0000 0.0000 0.0060 
<catboost.core.CatBoostClassifier at 0x7f9d356ec670>
CBoost = create_model("catboost")
  Accuracy AUC Recall Prec. F1 Kappa MCC
0 0.8286 0.9891 0.8286 0.8432 0.8313 0.8094 0.8106
1 0.8714 0.9897 0.8726 0.9094 0.8780 0.8571 0.8607
2 0.8429 0.9847 0.8494 0.8760 0.8422 0.8254 0.8291
3 0.6857 0.9609 0.6905 0.6993 0.6788 0.6512 0.6539
4 0.8571 0.9747 0.8560 0.8641 0.8558 0.8411 0.8423
5 0.7571 0.9721 0.7476 0.7964 0.7598 0.7299 0.7345
6 0.8286 0.9775 0.8333 0.8314 0.8226 0.8094 0.8111
7 0.9143 0.9927 0.9107 0.9210 0.9124 0.9046 0.9058
8 0.7714 0.9742 0.7679 0.8004 0.7733 0.7463 0.7490
9 0.8841 0.9956 0.8827 0.9026 0.8861 0.8711 0.8727
Mean 0.8241 0.9811 0.8239 0.8444 0.8240 0.8045 0.8070
SD 0.0647 0.0104 0.0647 0.0635 0.0661 0.0718 0.0713 
plt.figure(figsize=(10,8))
plot_model(CBoost, plot="auc")
#blues: 0, classical: 1, country: 2, disco: 3, hiphop: 4, jazz: 5, metal: 6, pop: 7, reggae: 8, rock: 9

plt.figure(figsize=(10,8))
plot_model(CBoost, plot="confusion_matrix")
#blues: 0, classical: 1, country: 2, disco: 3, hiphop: 4, jazz: 5, metal: 6, pop: 7, reggae: 8, rock: 9

plt.figure(figsize=(10,8))
plot_model(CBoost, plot='boundary')

plt.figure(figsize=(10,8))
plot_model(CBoost, plot = 'error')

predictions = predict_model(CBoost)
predictions = predictions.rename(columns={"label":"Actual Label","Label":"Predicted Label"})
predictions.head()
  Model Accuracy AUC Recall Prec. F1 Kappa MCC
0 CatBoost Classifier 0.8372 0.9799 0.8391 0.8459 0.8392 0.8187 0.8193
length chroma_stft_mean chroma_stft_var rms_mean rms_var spectral_centroid_mean spectral_centroid_var spectral_bandwidth_mean spectral_bandwidth_var rolloff_mean ... mfcc17_var mfcc18_mean mfcc18_var mfcc19_mean mfcc19_var mfcc20_mean mfcc20_var Actual Label Predicted Label Score
0 661794.0 0.293696 0.086162 0.146971 0.004145 2902.086182 335667.625000 2854.681885 153140.640625 6196.624512 ... 58.243416 -2.463421 43.542133 -1.034237 77.116264 -5.114805 93.542183 jazz jazz 0.9089
1 661794.0 0.273987 0.097541 0.069796 0.001864 1193.266235 134150.093750 1559.023682 51232.066406 2479.174561 ... 35.147602 -2.239031 30.978254 -1.454300 37.516171 -5.162262 69.798859 jazz jazz 0.9648
2 661794.0 0.490042 0.082950 0.092043 0.001892 1579.806396 522676.281250 2022.422852 258257.515625 3541.987061 ... 49.908375 0.655969 50.322571 -3.783521 41.039215 -0.574324 35.579788 reggae reggae 0.8702
3 661794.0 0.421436 0.078718 0.270942 0.001899 2442.871338 130742.023438 2358.614990 31688.839844 5267.699219 ... 38.263348 3.567072 33.319927 -5.962289 34.358105 -1.646955 59.907158 blues rock 0.5115
4 661794.0 0.396144 0.085204 0.235248 0.007127 2060.750244 229497.296875 2084.838379 81975.718750 4220.074707 ... 45.364784 2.656939 55.688828 -4.384609 47.885178 0.412821 61.150539 blues blues 0.7379

5 rows × 61 columns

final_CBoost = finalize_model(CBoost)